Acceptability of patient-centered digital vaccine safety monitoring: A Canadian Immunization Research Network study
Bibliographic record
Abstract
This study examined the willingness of Canadians to use patient-centered digital reporting solutions for adverse events following immunization (AEFI) reporting. We identified the preferred medium for reporting, and any privacy and confidentiality concerns among prospective users. A geographically diverse panel of 2,036 Canadian adults 18 y of age and older was surveyed online in September 2024. Descriptive statistics and a multivariable regression model were used to identify factors associated with a willingness to report AEFI. Among respondents 85% (n = 1724) indicated a willingness to report AEFI, and most (n = 1137, 56%) preferred to report AEFI only when they occurred as opposed to answering survey on a regular basis for a short duration after vaccination (n = 458, 22%). The largest proportion of respondents (n = 911, 45%) indicated a preference to use an online fillable form through a secure government website to report AEFI. Living with a disability, age over 24 y and having a great deal of confidence in scientists were all significantly associated with a willingness to report, while having a great deal of trust in pharmaceutical companies was inversely associated. Our results emphasize the importance of considering convenience, privacy and confidentiality, and trust in public institutions when developing patient-centered digital reporting systems for AEFI. Future research should explore income and ethnic disparities in willingness to report AEFI and the effect of tailoring reporting systems to public concerns on willingness to report.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".